Bayesian Variable Selection and Model Averaging using Bayesian Adaptive Sampling
Bayesian Adaptive Sampling Without Replacement for Variable Selection ...
Bayesian Adaptive Sampling for Bayesian Model Averaging and Variable S...
BAS: Bayesian Model Averaging using Bayesian Adaptive Sampling
Bayesian Outlier Detection
Fitting Generalized Linear Models and Bayesian marginal likelihood eva...
Independent Bernoulli prior on models that with constraints for model ...
Independent Bernoulli Prior Distribution for Models
Beta-Binomial Prior Distribution for Models
Beta-Prime Prior Distribution for Coefficients in BMA Model
Generalized g-Prior Distribution for Coefficients in BMA Models
Coefficients of a Bayesian Model Average object
Compute Credible Intervals for BAS regression coefficients from BAS ob...
Compute Credible (Bayesian Confidence) Intervals for a BAS predict obj...
Summaries for Out of Sample Prediction
BAS MCMC diagnostic plot
Find the global Empirical Bayes estimates for BMA
Empirical Bayes Prior Distribution for Coefficients in BMA Model
eplogprob.marg - Compute approximate marginal inclusion probabilities ...
eplogprob - Compute approximate marginal inclusion probabilities from ...
Fitted values for a BAS BMA objects
Post processing function to force constraints on interaction inclusion...
Families of G-Prior Distribution for Coefficients in BMA Models
Hald Data
Generalized hyper-g/n Prior Distribution for g for mixtures of g-prior...
Hyper-g-Prior Distribution for Coefficients in BMA Models
Confluent hypergeometric1F1 function
Gaussian hypergeometric2F1 function
Information Criterion Families of Prior Distribution for Coefficients ...
Images of models used in Bayesian model averaging
Intrinsic Prior Distribution for Coefficients in BMA Models
Jeffreys Prior Distribution for for Mixtures of g-Priors for Coeff...
Coerce a BAS list object into a matrix.
Coerce a BAS list object into a matrix.
Compound Confluent hypergeometric function of two variables
Plots the posterior distributions of coefficients derived from Bayesia...
Plot Bayesian Confidence Intervals
Plot Diagnostics for an BAS Object
Prediction Method for an object of class BAS
Prediction Method for an Object of Class basglm
Print a Summary of Bayesian Model Averaging objects from BAS
Protein Activity Data
Robust-Prior Distribution for Coefficients in BMA Model
Summaries of Bayesian Model Averaging objects from BAS
Generalized tCCH g-Prior Distribution for Coefficients in BMA Models
Test based Bayes Factors for BMA Models
Generalized g-Prior Distribution for Coefficients in BMA Models
Truncated Beta-Binomial Prior Distribution for Models
Truncated Poisson Prior Distribution for Models
Truncated Power Prior Distribution for Models
Truncated Compound Confluent Hypergeometric function
Uniform Prior Distribution for Models
Update BAS object using a new prior
Extract the variable names for a model from a BAS prediction object
Coerce a BAS list object of models into a matrix.
Package for Bayesian Variable Selection and Model Averaging in linear models and generalized linear models using stochastic or deterministic sampling without replacement from posterior distributions. Prior distributions on coefficients are from Zellner's g-prior or mixtures of g-priors corresponding to the Zellner-Siow Cauchy Priors or the mixture of g-priors from Liang et al (2008) <DOI:10.1198/016214507000001337> for linear models or mixtures of g-priors from Li and Clyde (2019) <DOI:10.1080/01621459.2018.1469992> in generalized linear models. Other model selection criteria include AIC, BIC and Empirical Bayes estimates of g. Sampling probabilities may be updated based on the sampled models using sampling w/out replacement or an efficient MCMC algorithm which samples models using a tree structure of the model space as an efficient hash table. See Clyde, Ghosh and Littman (2010) <DOI:10.1198/jcgs.2010.09049> for details on the sampling algorithms. Uniform priors over all models or beta-binomial prior distributions on model size are allowed, and for large p truncated priors on the model space may be used to enforce sampling models that are full rank. The user may force variables to always be included in addition to imposing constraints that higher order interactions are included only if their parents are included in the model. This material is based upon work supported by the National Science Foundation under Division of Mathematical Sciences grant 1106891. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
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